Artificial intelligence as an enabler for data-driven management of climate-smart agricultural production

Nathaniel Narra, Joni Kukkamäki, Olli Niemitalo, Otto Rosenberg and Iivari Kunttu

Chapter 11 in Multidisciplinary Movements in AI and Generative AI, 2025, pp 197-219 from Edward Elgar Publishing

Abstract: Changes in global food consumption patterns, demographic changes, and climate patterns are posing an increasing challenge to food production. National and international emissions goals and commitments compound the issue by necessitating a reduction in environmental impact while increasing food production. Climate Smart Agriculture (CSA) seeks to address the issue of sustainable agricultural practices through three pillars: a sustainable increase in productivity and incomes; adapting/building resilience; and reducing or removing negative climate impacts. Research, development, and adoption of developments in each pillar require a deeper understanding of the production system within the ecosystem context. Naturally, data is crucial in its role in being able to explore, quantify, and make results accessible. Observational data on dynamic variables of a field ecosystem are, despite their importance to CSA, mostly available only from agricultural research sites. Related indirect but more widely available data includes general weather and remote sensing data as well as farm management data. Artificial intelligence (AI) is particularly suitable for untangling the complex interdependencies between the variables of interest and the myriad of available data. By focusing on the case of Finland, this chapter demonstrates the value of AI and data in informing decision-making in climate-smart agricultural production.

Keywords: Artificial intelligence; AI; Enablers; Data-driven management; DDM; Climate-smart agricultural production; Smart agriculture; Climate smart agriculture; CSA (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035358656
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